Double Your Databricks ROI with These Proven Strategies

In today’s data-driven business world, organizations increasingly rely on platforms like Databricks to ingest and analyze massive volumes of data. Because of the competitive advantage that businesses can gain when leveraging modern data technology, companies are making large investments in platforms like Databricks — but with that investment, a hefty ROI is needed. With companies under increasing pressure to demonstrate ROI from their Databricks investments, optimizing how data flows across teams, optimizing workloads, and ensuring that insights can be delivered to the right people at the right time is needed more than ever. 

In this blog, we will discuss how organizations can optimize and leverage their Databricks environment to boost their ROI. 

Streamline data processing workflows

Building efficient data pipelines is essential for streamlining data processes and reducing your total Databricks spend.  Without optimization, data pipelines can become a source of delays, waste of valuable resources, and decreased consumption of data. Databricks has several automation tools available that can be valuable resources for organizations looking to cut down on time spent on repetitive tasks such as data ingestion, transformation, and validation. 

Some of those tools include: 

1. Delta live tables (DLT): Makes sure only the latest and most relevant data is processed, simplifying the ETL process 
2. Databricks jobs API: allows data teams to automate scheduling and monitoring
3. Auto-scaling and auto-termination: creates flexibility in your Databricks resourcing depending on the workload scope. Also terminates unused clusters, which helps reduce computing costs

While these are some of the tools available to Databricks users for data pipeline optimization, there are also some best practices that data teams can follow to optimize their processes. 

1. Partitioning: When you partition data, Databricks can read and process only the essential parts, speeding up queries and reducing overall costs
2. Caching data: Caching data allows datasets to be accessed in-memory, reducing the number of direct queries to Databricks and cutting compute costs
3. Right-sizing clusters: Estimating the exact amount of resources needed for specific workflows can be difficult to pinpoint. That's why Databricks offers auto-scaling to ensure that your organization is not over-allocating and paying for resources it's not using.

Maximize collaboration between data teams

One of the many great features of Databricks is its emphasis on being a data platform, not just a CDW. Because of this focus on being a platform, Databricks was designed with collaboration and sharing in mind. By leveraging these collaborative tools, companies can break down traditional data silos and allow data transparency across the organization. In this section, we’ll discuss some of the features that enhance collaboration with Databricks.

1. Delta Lake: Providing organizations with this centralized storage layer in Databricks enables a host of possibilities including leveraging AI efficiently. This central layer providers data engineers with a single source of truth to build out pipelines that reduces redundancy in work and discrepancies across user bases
2. Notebooks: One of the coolest features in Databricks is the use of Notebooks to help write, run, and collaborate on code used in building data pipelines or machine learning models. Notebooks also integrate with Python, allowing for the creation of data visualizations
3. Version control: Databricks integrates version control through Git, allowing teams to track changes in code and work on different versions of code. This enables multiple teams or people to work on projects at the same time

Leverage built-in machine learning

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While Databricks does a fantastic job of providing data for traditional analytics reporting, integrating machine learning (ML) models within Databricks allows data teams to take their impact to the next level. The scalable architecture and collaborative features discussed earlier in the blog enable teams to easily build and deploy ML models, driving real-time decision-making and ROI on the platform. 

Here are some examples of how companies can use ML in Databricks to transform their business. 

1. Maintenance scheduling: Manufacturing plants use their Overall Equipment Effectiveness (OEE) as a north star to evaluate their plant’s performance and efficiency. Maximizing OEE means reducing machine downtime as much as possible. ML models can help plants analyze when certain machines in different lines are expected to fail, allowing plant managers to schedule downtime for maintenance and repairs optimally.
2. Customer churn prediction: Companies focus heavily on retaining their existing customer base because keeping an existing client is always cheaper than gaining a new one. Companies leverage ML models to do this by analyzing customer behavior patterns and purchase histories to help identify customers that are a churn risk. By identifying these potential churn customers, companies can target these specific customers with target ads and personalized discounts that can increase customer retention and lower the cost of sale
3. Inventory demand: Supply chains are the heartbeat of all CPG and retail companies, but making mistakes when managing your supply chain and inventory can be very costly. Machine learning models in Databricks can be leveraged to forecast demand for different products for different regions, states, and stores. This allows retailers to gauge how much inventory they should carry more accurately, and it can also inform CPG companies how much product they should be producing

Improve user adoption and training

No matter how powerful Databricks is as a platform, it is only as good as the people and teams using it. If used correctly, Databricks can be a massively valuable tool for organizations looking to scale and maximize the use of data in their decision-making. Any tool used incorrectly can quickly become a headache for both the teams using the platform as well as their leadership. Investing in training and user adoption is crucial for maximizing the return on your Databricks investment. Without proper training, some of the most powerful features of Databricks may go unused.

When teams are properly trained in Databricks, users can complete tasks quickly and correctly. When teams are able to produce actionable results in a timely manner, organizations can also reduce their dependence on third-party vendors, which has both short-term and long-term benefits for a data organization. This is why establishing internal training programs and identifying Databricks “champions” within your organization is crucial. 

While Databricks can be easy to adopt, users still need training to understand how to leverage the tools available to them most effectively. Untrained users can be prone to overusing computing resources, creating inefficient queries and pipelines, and using tools incorrectly. Luckily, Databricks offers a wealth of learning resources with its Databricks Academy to ensure data teams can skill up on the platform and make the most of its features. Creating persona-specific training paths can also help ensure that everyone in your organization gets the more applicable and appropriate training for their role. 

Maximizing use of data in business processes

While Databricks is an extremely valuable platform on its own, data is not valuable unless it is being used in a meaningful and timely manner. This is where using Sigma + Databricks can be the ultimate combination to leveraging data ingested, transformed, and modeled in Databricks. 

Sigma is a modern cloud-native BI tool that integrates seamlessly with Databricks, allowing users to access the data they need easily in real time. All of Sigma’s dashboards are built using live queries, so you know that the data you view is the latest in Databricks. This strategy also ensures a Single Source of Truth across your organization, which allows your CDW to be seen as a valuable data asset rather than a liability.

Additionally, Sigma is extremely easy to use. Traditional BI tools often require months, if not years, to gain mastery of the tool — Sigma lowers the barrier to entry with its extremely straightforward and logical approach to building data products. When more users are able to use trusted data in their daily business processes confidently, this creates a greater demand for data in business lines, which ultimately drives the ROI in Databricks. 

Strategic optimization: Unlocking the full value of Databricks

Maximizing the potential of Databricks requires more than just procuring the platform - it involves a strategic approach that optimizes performance, lowers cost, fosters organizational collaboration, and ensures that teams that need data can access it on time. 

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